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Executive Summary:

In late 2024 an Indian healthcare provider experienced a severe cybersecurity attack that demonstrated how powerful AI ransomware is. This blog discusses the background to the attack, how it took place and the effects it caused (both medical and financial), how organisations reacted, and the final result of it all, stressing on possible dangers in the healthcare industry with a lack of sufficiently adequate cybersecurity measures in place. The incident also interrupted the normal functioning of business and explained the possible economic and image losses from cyber threats. Other technical results of the study also provide more evidence and analysis of the advanced AI malware and best practices for defending against them. 

1. Introduction

The integration of artificial intelligence (AI) in cybersecurity has revolutionised both defence mechanisms and the strategies employed by cybercriminals. AI-powered attacks, particularly ransomware, have become increasingly sophisticated, posing significant threats to various sectors, including healthcare. This report delves into a case study of an AI-powered ransomware attack on a prominent Indian healthcare provider in 2024, analysing the attack's execution, impact, and the subsequent response, along with key technical findings.

2. Background 

 In late 2024, a leading healthcare organisation in India which is involved in the research and development of AI techniques fell prey to a ransomware attack that was AI driven to get the most out of it. With many businesses today relying on data especially in the healthcare industry that requires real-time operations, health care has become the favourite of cyber criminals. AI aided attackers were able to cause far more detailed and damaging attack that severely affected the operation of the provider whilst jeopardising the safety of the patient information.  

 3. Attack Execution 

The attack began with the launch of a phishing email designed to target a hospital administrator. They received an email with an infected attachment which when clicked in some cases injected the AI enabled ransomware into the hospitals network. AI incorporated ransomware was not as blasé as traditional ransomware, which sends copies to anyone, this studied the hospital’s IT network. First, it focused and targeted important systems which involved implementation of encryption such as the electronic health records and the billing departments. 

The fact that the malware had an AI feature allowed it to learn and adjust its way of propagation in the network, and prioritise the encryption of most valuable data. This accuracy did not only increase the possibility of the potential ransom demand but also it allowed reducing the risks of the possibility of early discovery. 

 4. Impact 

•  The consequences of the attack were immediate and severe: The consequences of the attack were immediate and severe.
•  Operational Disruption: The centralization of important systems made the hospital cease its functionality through the acts of encrypting the respective components. Operations such as surgeries, routine medical procedures and admitting of patients were slowed or in some cases referred to other hospitals. 
•  Data Security: Electronic patient records and associated billing data became off-limit because of the vulnerability of patient confidentiality. The danger of data loss was on the verge of becoming permanent, much to the concern of both the healthcare provider and its patients. 
•  Financial Loss: The attackers asked for 100 crore Indian rupees (approximately 12 USD million) for the decryption key. Despite the hospital not paying for it, there were certain losses that include the operational loss due to the server being down, loss incurred by the patients who were affected in one way or the other, loss incurred in responding to such an incident and the loss due to bad reputation. 

5. Response

As soon as the hotel’s management was informed about the presence of ransomware, its IT department joined forces with cybersecurity professionals and local police. The team decided not to pay the ransom and instead recover the systems from backup. Despite the fact that this was an ethically and strategically correct decision, it was not without some challenges. Reconstruction was gradual, and certain elements of the patients’ records were permanently erased. 

 In order to avoid such attacks in the future, the healthcare provider put into force several organisational and technical actions such as network isolation and increase of cybersecurity measures. Even so, the attack revealed serious breaches in the provider’s IT systems security measures and protocols. 

6. Outcome

The attack had far-reaching consequences:

• Financial Impact: A healthcare provider suffers a lot of crashes in its reckoning due to substantial service disruption as well as bolstering cybersecurity and compensating patients. 
•  Reputational Damage: The leakage of the data had a potential of causing a complete loss of confidence from patients and the public this affecting the reputation of the provider. This, of course, had an effect on patient care, and ultimately resulted in long-term effects on revenue as patients were retained. 
•  Industry Awareness: The breakthrough fed discussions across the country on how to improve cybersecurity provisions in the healthcare industry. It woke up the other care providers to review and improve their cyber defence status. 

7. Technical Findings

The AI-powered ransomware attack on the healthcare provider revealed several technical vulnerabilities and provided insights into the sophisticated mechanisms employed by the attackers. These findings highlight the evolving threat landscape and the importance of advanced cybersecurity measures.

7.1 Phishing Vector and Initial Penetration

• Sophisticated Phishing Tactics: The phishing email was crafted with precision, utilising AI to mimic the communication style of trusted contacts within the organisation. The email bypassed standard email filters, indicating a high level of customization and adaptation, likely due to AI-driven analysis of previous successful phishing attempts.
• Exploitation of Human Error: The phishing email targeted an administrative user with access to critical systems, exploiting the lack of stringent access controls and user awareness. The successful penetration into the network highlighted the need for multi-factor authentication (MFA) and continuous training on identifying phishing attempts.

7.2 AI-Driven Malware Behavior

• Dynamic Network Mapping: Once inside the network, the AI-powered malware executed a sophisticated mapping of the hospital's IT infrastructure. Using machine learning algorithms, the malware identified the most critical systems—such as Electronic Health Records (EHR) and the billing system—prioritising them for encryption. This dynamic mapping capability allowed the malware to maximise damage while minimising its footprint, delaying detection.
• Adaptive Encryption Techniques: The malware employed adaptive encryption techniques, adjusting its encryption strategy based on the system's response. For instance, if it detected attempts to isolate the network or initiate backup protocols, it accelerated the encryption process or targeted backup systems directly, demonstrating an ability to anticipate and counteract defensive measures.
• Evasive Tactics: The ransomware utilised advanced evasion tactics, such as polymorphic code and anti-forensic features, to avoid detection by traditional antivirus software and security monitoring tools. The AI component allowed the malware to alter its code and behaviour in real time, making signature-based detection methods ineffective.

7.3 Vulnerability Exploitation

• Weaknesses in Network Segmentation: The hospital’s network was insufficiently segmented, allowing the ransomware to spread rapidly across various departments. The malware exploited this lack of segmentation to access critical systems that should have been isolated from each other, indicating the need for stronger network architecture and micro-segmentation.
• Inadequate Patch Management: The attackers exploited unpatched vulnerabilities in the hospital’s IT infrastructure, particularly within outdated software used for managing patient records and billing. The failure to apply timely patches allowed the ransomware to penetrate and escalate privileges within the network, underlining the importance of rigorous patch management policies.

7.4 Data Recovery and Backup Failures

• Inaccessible Backups: The malware specifically targeted backup servers, encrypting them alongside primary systems. This revealed weaknesses in the backup strategy, including the lack of offline or immutable backups that could have been used for recovery. The healthcare provider’s reliance on connected backups left them vulnerable to such targeted attacks.
• Slow Recovery Process: The restoration of systems from backups was hindered by the sheer volume of encrypted data and the complexity of the hospital’s IT environment. The investigation found that the backups were not regularly tested for integrity and completeness, resulting in partial data loss and extended downtime during recovery.

7.5 Incident Response and Containment

• Delayed Detection and Response: The initial response was delayed due to the sophisticated nature of the attack, with traditional security measures failing to identify the ransomware until significant damage had occurred. The AI-powered malware’s ability to adapt and camouflage its activities contributed to this delay, highlighting the need for AI-enhanced detection and response tools.
• Forensic Analysis Challenges: The anti-forensic capabilities of the malware, including log wiping and data obfuscation, complicated the post-incident forensic analysis. Investigators had to rely on advanced techniques, such as memory forensics and machine learning-based anomaly detection, to trace the malware’s activities and identify the attack vector.

8. Recommendations Based on Technical Findings

To prevent similar incidents, the following measures are recommended:

1. AI-Powered Threat Detection: Implement AI-driven threat detection systems capable of identifying and responding to AI-powered attacks in real time. These systems should include behavioural analysis, anomaly detection, and machine learning models trained on diverse datasets.
2. Enhanced Backup Strategies: Develop a more resilient backup strategy that includes offline, air-gapped, or immutable backups. Regularly test backup systems to ensure they can be restored quickly and effectively in the event of a ransomware attack.
3. Strengthened Network Segmentation: Re-architect the network with robust segmentation and micro-segmentation to limit the spread of malware. Critical systems should be isolated, and access should be tightly controlled and monitored.
4. Regular Vulnerability Assessments: Conduct frequent vulnerability assessments and patch management audits to ensure all systems are up to date. Implement automated patch management tools where possible to reduce the window of exposure to known vulnerabilities.
5. Advanced Phishing Defences: Deploy AI-powered anti-phishing tools that can detect and block sophisticated phishing attempts. Train staff regularly on the latest phishing tactics, including how to recognize AI-generated phishing emails.

9. Conclusion

The AI empowered ransomware attack on the Indian healthcare provider in 2024 makes it clear that the threat of advanced cyber attacks has grown in the healthcare facilities. Sophisticated technical brief outlines the steps used by hackers hence underlining the importance of ongoing active and strong security. This event is a stark message to all about the importance of not only remaining alert and implementing strong investments in cybersecurity but also embarking on the formulation of measures on how best to counter such incidents with limited harm. AI is now being used by cybercriminals to increase the effectiveness of the attacks they make and it is now high time all healthcare organisations ensure that their crucial systems and data are well protected from such attacks.

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In a recent ruling, a U.S. federal judge sided with Meta in a copyright lawsuit brought by a group of prominent authors who alleged that their works were illegally used to train Meta’s LLaMA language model. While this seems like a significant legal victory for the tech giant, it may not be so. Rather, this is a good case study for creators in the USA to refine their legal strategies and for policymakers worldwide to act quickly to shape the rules of engagement between AI and intellectual property.

The Case: Meta vs. Authors

In Kadrey v. Meta, the plaintiffs alleged that Meta trained its LLaMA models on pirated copies of their books, violating copyright law. However, U.S. District Judge Vince Chhabria ruled that the authors failed to prove two critical things: that their copyrighted works had been used in a way that harmed their market and that such use was not “transformative.” In fact, the judge ruled that converting text into numerical representations to train an AI was sufficiently transformative under the U.S. fair use doctrine. He also noted that the authors’ failure to demonstrate economic harm undermined their claims. Importantly, he clarified that this ruling does not mean that all AI training data usage is lawful, only that the plaintiffs didn’t make a strong enough case.

Meta even admitted that some data was sourced from pirate sites like LibGen, but the Judge still found that fair use could apply because the usage was transformative and non-exploitative.

A Tenuous Win

Chhabria’s decision emphasised that this is not a blanket endorsement of using copyrighted content in AI training. The judgment leaned heavily on the procedural weakness of the case and not necessarily on the inherent legality of Meta’s practices.

Policy experts are warning that U.S. courts are currently interpreting AI training as fair use in narrow cases, but the rulings may not set the strongest judicial precedent. The application of law could change with clearer evidence of commercial harm or a more direct use of content. 

Moreover, the ruling does not address whether authors or publishers should have the right to opt out of AI model training, a concern that is gaining momentum globally.

Implications for India

The case highlights a glaring gap in India’s copyright regime: it is outdated. Since most AI companies are located in the U.S., courts have had the opportunity to examine copyright in the context of AI-generated content. India has yet to start. Recently, news agency ANI filed a case alleging copyright infringement against OpenAI for training on its copyrighted material. However, the case is only at an interim stage. The final outcome of the case will have a significant impact on the legality of these language models being able to use copyrighted material for training.

Considering that India aims to develop “state-of-the-art foundational AI models trained on Indian datasets” under the IndiaAI Mission, the lack of clear legal guidance on what constitutes fair dealing when using copyrighted material for AI training is a significant gap.  

Thus, key points of consideration for policymakers include: 

• Need for Fair Dealing Clarity: India’s fair-dealing provisions under the Copyright Act, 1957, are narrower than U.S. fair use. The doctrine may have to be reviewed to strike a balance between this law and the requirement of diverse datasets to develop foundational models rooted in Indian contexts. A parallel concern regarding data privacy also arises. 
• Push for Opt-Out or Licensing Mechanisms: India should consider whether to introduce a framework that requires companies to license training data or provide an opt-out system for creators, especially given the volume of Indian content being scraped by global AI systems.
• Digital Public Infrastructure for AI: India’s policymakers could take this opportunity to invest in public datasets, especially in regional languages, that are both high quality and legally safe for AI training.
• Protecting Local Creators: India needs to ensure that its authors, filmmakers, educators and journalists are protected from having their work repurposed without compensation, since power asymmetries between Big Tech and local creators can lead to exploitation of the latter.

Conclusion

The ruling in Meta’s favour is just one win for the developer. The real questions about consent, compensation and creative control remain unanswered. Meanwhile, the lesson for India is urgent: it needs AI policies that balance innovation with creator rights and provide legal certainty and ethical safeguards as it accelerates its AI ecosystem. Further, as global tech firms race ahead, India must not remain a passive data source; it must set the terms of its digital future. This will help the country move a step closer to achieving its goal of building sovereign AI capacity and becoming a hub for digital innovation.

References

• https://www.theguardian.com/technology/2025/jun/26/meta-wins-ai-copyright-lawsuit-as-us-judge-rules-against-authors 
• https://www.wired.com/story/meta-scores-victory-ai-copyright-case/ 
• https://www.cnbc.com/2025/06/25/meta-llama-ai-copyright-ruling.html 
• https://www.mondaq.com/india/copyright/1348352/what-is-fair-use-of-copyright-doctrine 
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2113095#:~:text=One%20of%20the%20key%20pillars,models%20trained%20on%20Indian%20datasets. ‍
• https://www.ndtvprofit.com/law-and-policy/ani-vs-openai-delhi-high-court-seeks-responses-on-copyright-infringement-charges-against-chatgpt

In the vast, uncharted territories of the digital world, a sinister phenomenon is proliferating at an alarming rate. It's a world where artificial intelligence (AI) and human vulnerability intertwine in a disturbing combination, creating a shadowy realm of non-consensual pornography. This is the world of deepfake pornography, a burgeoning industry that is as lucrative as it is unsettling.

According to a recent assessment, at least 100,000 deepfake porn videos are readily available on the internet, with hundreds, if not thousands, being uploaded daily. This staggering statistic prompts a chilling question: what is driving the creation of such a vast number of fakes? Is it merely for amusement, or is there a more sinister motive at play?

Recent Trends and Developments 

An investigation by India Today’s Open-Source Intelligence (OSINT) team reveals that deepfake pornography is rapidly morphing into a thriving business. AI enthusiasts, creators, and experts are extending their expertise, investors are injecting money, and even small financial companies to tech giants like Google, VISA, Mastercard, and PayPal are being misused in this dark trade. Synthetic porn has existed for years, but advances in AI and the increasing availability of technology have made it easier—and more profitable—to create and distribute non-consensual sexually explicit material. The 2023 State of Deepfake report by Home Security Heroes reveals a staggering 550% increase in the number of deepfakes compared to 2019. 

What’s the Matter with Fakes? 

But why should we be concerned about these fakes? The answer lies in the real-world harm they cause. India has already seen cases of extortion carried out by exploiting deepfake technology. An elderly man in UP’s Ghaziabad, for instance, was tricked into paying Rs 74,000 after receiving a deep fake video of a police officer. The situation could have been even more serious if the perpetrators had decided to create deepfake porn of the victim.

The danger is particularly severe for women. The 2023 State of Deepfake Report estimates that at least 98 percent of all deepfakes is porn and 99 percent of its victims are women. A study by Harvard University refrained from using the term “pornography” for creating, sharing, or threatening to create/share sexually explicit images and videos of a person without their consent. “It is abuse and should be understood as such,” it states.

Based on interviews of victims of deepfake porn last year, the study said 63 percent of participants talked about experiences of “sexual deepfake abuse” and reported that their sexual deepfakes had been monetised online. It also found “sexual deepfake abuse to be particularly harmful because of the fluidity and co-occurrence of online offline experiences of abuse, resulting in endless reverberations of abuse in which every aspect of the victim’s life is permanently disrupted”.

Creating deepfake porn is disturbingly easy. There are largely two types of deepfakes: one featuring faces of humans and another featuring computer-generated hyper-realistic faces of non-existing people. The first category is particularly concerning and is created by superimposing faces of real people on existing pornographic images and videos—a task made simple and easy by AI tools.

During the investigation, platforms hosting deepfake porn of stars like Jennifer Lawrence, Emma Stone, Jennifer Aniston, Aishwarya Rai, Rashmika Mandanna to TV actors and influencers like Aanchal Khurana, Ahsaas Channa, and Sonam Bajwa and Anveshi Jain were encountered. It takes a few minutes and as little as Rs 40 for a user to create a high-quality fake porn video of 15 seconds on platforms like FakeApp and FaceSwap.

The Modus Operandi 

These platforms brazenly flaunt their business association and hide behind frivolous declarations such as: the content is “meant solely for entertainment” and “not intended to harm or humiliate anyone”. However, the irony of these disclaimers is not lost on anyone, especially when they host thousands of non-consensual deepfake pornography.

As fake porn content and its consumers surge, deepfake porn sites are rushing to forge collaborations with generative AI service providers and have integrated their interfaces for enhanced interoperability. The promise and potential of making quick bucks have given birth to step-by-step guides, video tutorials, and websites that offer tools and programs, recommendations, and ratings.

Nearly 90 per cent of all deepfake porn is hosted by dedicated platforms that charge for long-duration premium fake content and for creating porn—of whoever a user wants, and take requests for celebrities. To encourage them further, they enable creators to monetize their content.

One such website, Civitai, has a system in place that pays “rewards” to creators of AI models that generate “images of real people'', including ordinary people. It also enables users to post AI images, prompts, model data, and LoRA (low-rank adaptation of large language models) files used in generating the images. Model data designed for adult content is gaining great popularity on the platform, and they are not only targeting celebrities. Common people are equally susceptible.

Access to premium fake porn, like any other content, requires payment. But how can a gateway process payment for sexual content that lacks consent? It seems financial institutes and banks are not paying much attention to this legal question. During the investigation, many such websites accepting payments through services like VISA, Mastercard, and Stripe were found.

Those who have failed to register/partner with these fintech giants have found a way out. While some direct users to third-party sites, others use personal PayPal accounts to manually collect money in the personal accounts of their employees/stakeholders, which potentially violates the platform's terms of use that ban the sale of “sexually oriented digital goods or content delivered through a digital medium.”

 Among others, the MakeNude.ai web app – which lets users “view any girl without clothing” in “just a single click” – has an interesting method of circumventing restrictions around the sale of non-consensual pornography. The platform has partnered with Ukraine-based Monobank and Dublin’s BetaTransfer Kassa which operates in “high-risk markets”.

BetaTransfer Kassa admits to serving “clients who have already contacted payment aggregators and received a refusal to accept payments, or aggregators stopped payments altogether after the resource was approved or completely freeze your funds”. To make payment processing easy, MakeNude.ai seems to be exploiting the donation ‘jar’ facility of Monobank, which is often used by people to donate money to Ukraine to support it in the war against Russia.

The Indian Scenario 

India currently is on its way to design dedicated legislation to address issues arising out of deepfakes. Though existing general laws requiring such platforms to remove offensive content also apply to deepfake porn. However, persecution of the offender and their conviction is extremely difficult for law enforcement agencies as it is a boundaryless crime and sometimes involves several countries in the process.

A victim can register a police complaint under provisions of Section 66E and Section 66D of the IT Act, 2000. Recently enacted Digital Personal Data Protection Act, 2023 aims to protect the digital personal data of users. Recently Union Government issued an advisory to social media intermediaries to identify misinformation and deepfakes. Comprehensive law promised by Union IT minister Ashwini Vaishnav will be able to address these challenges.

Conclusion 

In the end, the unsettling dance of AI and human vulnerability continues in the dark web of deepfake pornography. It's a dance that is as disturbing as it is fascinating, a dance that raises questions about the ethical use of technology, the protection of individual rights, and the responsibility of financial institutions. It's a dance that we must all be aware of, for it is a dance that affects us all.

References

• https://www.indiatoday.in/india/story/deepfake-porn-artificial-intelligence-women-fake-photos-2471855-2023-12-04
• https://www.hindustantimes.com/opinion/the-legal-net-to-trap-peddlers-of-deepfakes-101701520933515.html
• https://indianexpress.com/article/opinion/columns/with-deepfakes-getting-better-and-more-alarming-seeing-is-no-longer-believing/